Background: The TGF-β signaling pathway is an essential regulator of many cellular process involved in carcinogenesis. Smad proteins are central to the function of TGF-β signaling. In this study, we evaluated genetic variation in TGFβ1, TGFβR1, Smad1, Smad2, Smad3, and Smad4 and risk of colon and rectal cancer.

Methods: Data are from a large case–control study of colon (n = 1,444 cases, 1,841 controls) and rectal (n = 754 cases, 856 controls) cancer participants with DNA.

Results: Both TGFβ1 rs1800469 and rs4803455 were associated with colon cancer [odds ratio (OR) = 0.65 and 1.43, 95% CI = 0.51–0.84 and 1.18–1.73, respectively) but not rectal cancer. Likewise, 1 of 3 tagSNPs for TGFβR1, 2 of the 4 tagSNPs for Smad2, and 4 of 37 Smad3 tagSNPs were associated with colon cancer. Fewer significant associations were observed for rectal cancer, with only 1 tagSNP in Smad2 and 3 tagSNP in Smad3 having 95% CIs excluding 1.0. Several Smad3 tagSNPs were only associated with CpG island methylator phenotype. We observed several statistically significant interactions between genetic variation in the TGF-β signaling pathway and NFκB1, further illustrating its involvement in proposed mechanisms. In addition, we observed statistically significant interaction between TGFβ1, TGFβR1, and Smad3 and cigarette smoking, aspirin use, and estrogen status for both colon and rectal cancers. Variation in TGFβ1, TGFβR1, and Smad3 seemed to influence survival after diagnosis of colon and rectal cancer.

Conclusions: These findings provide further support for genetic variation in the TGF-β signaling pathway and risk of developing both colon and rectal cancers.

Impact: Insight into biological pathways is provided. Cancer Epidemiol Biomarkers Prev; 20(1); 57–69. ©2011 AACR.

The TGF-β signaling pathway is an essential regulator of cellular proliferation, differentiation, apoptosis, and extracellular matrix remodeling in the cell (1). In addition, this signaling pathway is involved in angiogenesis and inflammation. It mediates intracellular actions of proinflammatory cytokines, including activation of nuclear factor-kappa B (NFκB; refs. 2, 3) and deficiency of TGF-β has been shown to lead to extensive inflammation (2). TGF-β ligands initiate their cellular effects by binding to cell surface receptors (1); type 1 receptors mediate their cellular effects through interaction with Smad proteins. Thus, Smads are key intracellular mediators of the transcriptional responses to TGF-β (4).

Smad4 (DPC4) is inactivated in some colorectal cancers (CRC), and germline mutations of Smad4 have been linked to familial juvenile polyposis families (5). Smad2 has been identified as a TGF-β–responsive Smad that is a transcription factor involved in the regulation of cell growth and apoptosis. Smad7 is also involved in inflammation-related pathways and has been shown to modulate TGF-β and Wnt signaling (6). Genetic variation in the Smad7 gene on 8q21 has been identified through numerous genome-wide association studies (GWAS) as being associated with CRC (7). Like Smad7, Smad2 and Smad4 are located on 8q21. We previously reported on the replication of tagSNPs in the Smad7 gene identified from GWAS in our population-based case–control study of colon cancer (8). We observed that rs12953717 was associated with a statistically significant increased risk of colon cancer [odds ratio (OR) = 1.38, 95% CI = 1.13–1.68; P linear trend < 0.01) for the TT genotype compared with the CC genotype whereas the CC genotype of the rs4939827 tagSNP was inversely associated with colon cancer (OR = 0.77, 95% CI = 0.64–0.93) relative to the TT genotype. In our study, associations seemed to be modified by use of aspirin (8).

There is growing support for the role of the TGF-β signaling pathway in the etiology of colon and rectal cancer. In this study, we evaluate genetic variation in TGFβ1, TGFβR1, Smad1, Smad2, Smad3, and Smad4. We evaluate how these genes interact with other potentially important genes in the pathway, including Smad7, NFκB1, and IKBkB, involved in inflammation-related mechanisms. Environmental factors that may operate in this pathway include estrogen, aspirin/non-steroidal anti-inflammatory drugs (NSAID), and cigarette smoking that may lead to oxidative stress and increase the likelihood of inflammation (9). We evaluate the potential interactions between these factors and genetic variation in the TGF-β signaling pathway. In addition, we seek to confirm previous reports that genetic alterations in the TGF-β signaling pathway influences tumor markers such as microsatellite instability and epigenetic changes. We evaluate the hypothesis that the TGF-β signaling influences prognosis after diagnosis with cancer by comparing survival rates based on genetic variation in this pathway.

Two study populations are included in these analyses. The first study, a population-based case–control study of colon cancer, included cases (n = 1,593) and controls (n = 1,994) identified between October 1, 1991, and September 30, 1994 (10), living in the Twin Cities Metropolitan Area, Kaiser Permanente Medical Care Program of Northern California (KPMCP), and a 7-county area of Utah. The second study, with identical data collection methods, included cases with cancer of the rectosigmoid junction or rectum (n = 790) and controls (n = 999) who were identified between May 1997 and May 2001 in Utah and KPMCP (11). Eligible cases were between 30 and 79 years old at time of diagnosis, English speaking, mentally competent to complete the interview, had no previous history of CRC, and no known (as indicated on the pathology report) familial adenomatous polyposis, ulcerative colitis, or Cohn's disease.

Controls were matched to cases by gender and by 5-year age groups. At KPMCP, controls were randomly selected from membership lists; in Utah, controls 65 years and older were randomly selected from the Health Care Financing Administration lists and controls younger than 65 years were randomly selected from driver's license lists. In Minnesota, controls were selected from driver's license and state-identification lists. Study details have been previously reported (12, 13).

Interview data collection

Data were collected by trained and certified interviewers using laptop computers. All interviews were audio-taped as previously described and reviewed for quality control purposes (14). The referent period for the study was 2 years prior to diagnosis for cases or selection for controls. Detailed information was collected on diet, physical activity, medical history, reproductive history, family history of cancer in first-degree relatives, regular use of aspirin and NSAIDs, and body size.

Tumor registry data

Tumor registry data were obtained to determine disease stage at diagnosis and months of survival after diagnosis. Disease stage was categorized by Surveillance, Epidemiology, and End Results (SEER) staging of local, regional, and distant disease as well as by the American Joint Committee on Cancer (AJCC) staging criteria. Local tumor registries provided information on patient follow-up including vital status, cause of death, and contributing cause of death. Survival months were calculated on the basis of month and year of diagnosis, and month and year of death, or date of last contact for those individuals who were still alive.

Tumor marker data

We have previously evaluated tumors for CpG island methylator phenotype (CIMP), microsatellite instability (MSI), TP53 mutations, and KRAS2 mutations (15–18) and were therefore able to evaluate genes in relation to tumors with specific characteristics or markers. Details for methods used to evaluate these epigenetic and genetic changes have been described in previous publications (15–18).

tagSNP selection and genotyping

tagSNPs were selected for genes TGFβR1, Smad1, Smad2, Smad3, and Smad4, using the following parameters: an r2 < 0.8 defined LD blocks using a Caucasian LD map, minor allele frequency or MAF > 0.1, range = −1,500 bp from the initiation codon to +1,500 bp from the termination codon, and 1 SNP/LD bin. All markers were genotyped using a multiplexed bead array assay format based on GoldenGate chemistry (Illumina). A genotyping call rate of 99.85% was attained. Blinded internal replicates represented 4.4% of the sample set. The duplicate concordance rate was 100.00%.

For TGFβ1, candidate markers rs1800469 and rs4803455 were chosen on the basis of prevalent MAF and previous findings described in the literature (19); rs1800469 and rs4803455 were genotyped independently using a TaqMan assay from Applied Biosystems. Each 5-μL PCR reaction contained 20 ng of genomic DNA, primers, probes, and TaqMan Universal PCR Master Mix (containing AmpErase UNG, AmpliTaq Gold enzyme, dNTPs, and reaction buffer). PCR was carried out under the following conditions: 50°C for 2 minutes to activate UNG, 95°C for 10 minutes, followed by 40 cycles of 92°C for 15 seconds, and 60°C for 1 minute using 384 well duel block ABI 9700. Fluorescent endpoints of the TaqMan reactions were measured using a 7900HT sequence detection instrument.

Statistical methods

All statistical analyses were carried out using SAS version 9.2 (SAS Institute). We assessed ORs and 95% CIs in multiple logistic regression models for colon and rectal cancer separately. All SNPs were evaluated first by comparing the heterozygote and homozygote variants to the homozygote wild-type and subsequently assessing the likelihood of the dominant and recessive models of inheritance; the best fitting model is presented (20). P values from the unadjusted Max test were used to adjust for multiple comparisons of tagSNPs using the methods by Conneely and Boehnke (20, 21). Minimal adjustments were made for age, sex, race, and study center. Additional adjustments for BMI (kg/m2), physical activity, use of aspirin or NSAIDs within 2 years of the referent period, and cigarette smoking status (ever or never regularly smoked) did not alter associations.

Stepwise regression models were used to identifying tagSNPs that contributed uniquely and most significantly to the overall fit of the model for colon and rectal as well as to identify potential confounding of tagSNPs within genes. Inclusion in the stepwise regression model was based on a score chi-square significance level of 0.05 whereas exclusion was determined on the basis of a Wald chi-square 0.05 significance level. Subsequent analysis for interaction was based both on tagSNPs remaining in the final stepwise model and those identified as being important independently.

We evaluate interaction between TGFβ1 and its receptor and Smad1, Smad2, Smad3, Smad4, Smad7, IKBκB, and NFκB1. Possible interactions between SNPs and sex, age (30–64 or 65–79), recent aspirin or NSAID use, estrogen status, BMI (<25, 25–30, >30), and cigarette smoking were evaluated given the hypothesized mechanisms proposed for these genes. Associations between colon cancer and Smad7, IKBkB, and NFκB1 have been previously reported (8; K. Curtin, R.K. Wolff, J.S. Herrick, R. Abo, M.L. Slattery, unpublished data). P values for interaction were determined by comparing a full model including an ordinal multiplicative interaction term to a reduced model without an interaction term using a likelihood ratio test; a categorical model was used for TGFβ1 rs4803455 and smoking and for Smad2 rs1792689 and TGFβR1 rs1571590. Haplotypes based on the SNPs being identified as significant for each gene were examined with both environmental and gene interactions but did not yield any more meaningful results than looking at the individual SNPs and therefore are excluded.

Tumors were defined by specific alterations detected; any TP53 mutation, any KRAS2 mutation, MSI+, or CIMP+ defined as at least 2 of 5 markers methylated. As the proportion of MSI+ tumors in the rectal cases was <3% (22), there was insufficient power to examine these tumor markers with genotype data. Population-based controls were used to assess associations for the population overall when examining multiple outcomes defined by tumor status. In addition to identifying variants that contributed to a given phenotype independently, a stepwise regression of all SNPs per gene was implemented in SAS, using the logistic procedure for each individual tumor type.

Time of survival was determined on the basis of date of diagnosis and date of last contact or death, truncated at 5 years, the time period which is most meaningful for assessment of impact with CRC. Associations between SNPs and risk of dying of CRC within 5 years from diagnosis were evaluated using Cox proportional hazards models to provide multivariate hazard rate ratios (HRR) and 95% CIs adjusted for age at diagnosis, study center, race, sex, AJCC stage, and tumor markers. HRRs were assessed for SNPs independently and using stepwise regression via the phreg procedure adjusting for other SNPs.

Table 1 describes the genes and corresponding SNPs associated independently, through interaction, or with tumor markers. All SNPs were in Hardy–Weinberg equilibrium (HWE). SNPs that were independently associated with colon or rectal cancer overall are shown in Figure 1. As shown in the figure, the following associations were observed for colon cancer: OR = 1.25 (95% CI = 1.03–1.51) TT versus AA for Smad2 rs1787199; OR = 1.33 (95% CI = 1.06–1.67) CC versus TT for Smad2 rs4940086; OR = 0.68 (95% CI = 0.55–0.85) for AG/GG versus AA for Smad3 rs12901071; OR = 0.69 (95% CI = 0.57–0.84) CC versus AA for Smad3 rs1498506; OR = 0.76 (95% CI = 0.59–0.98) for AA versus GG/GA for Smad3 rs7163381, adjusted for rs1498506; OR = 0.68 (95% CI = 0.47–0.97) CC versus GG/GC for Smad3 rs2414937; OR = 0.65 (95% CI = 0.51–0.84) for AA versus GG for TGFβ1 rs1800469; OR = 1.43 (95% CI = 1.18–1.73) for AA versus CC for TGFβ1 rs4803455; OR = 0.85 (95% CI = 0.74–0.99) for TA/AA versus TT for TGFBR1 rs6478974. After adjustment for multiple comparisons, Smad3 rs1498506 and rs12901071 remained statistically significant (adjusted P values of 0.0.009 and 0.015, respectively). Because TGFβ1 rs1800469 and rs4803455 were candidate SNPs, we did not adjust them for multiple comparisons.

Figure 1.

Associations between SNPs in the TGF-β signaling pathway and colon and rectal cancer.

Figure 1.

Associations between SNPs in the TGF-β signaling pathway and colon and rectal cancer.

Close modal
Table 1.

Summary of SNPs

ColonRectal
GeneAliasLocationSNPMajor/Minor alleleMAFaHeterozygote OR (95% CI)Homozygote rare OR (95% CI)Heterozygote OR (95% CI)Homozygote rare OR (95% CI)
Smad2 MAD2 18q21.1 rs1787199 A/T 0.46 1.08 (0.92–1.26) 1.24 (1.03–1.51) 0.85 (0.69–1.05)b  
 MADH2  rs1792689 C/T 0.13 0.96 (0.82–1.12) 1.30 (0.79–2.13) 0.78 (0.62–0.98)b  
 JV18  rs4940086 T/C 0.33 1.08 (0.94–1.25) 1.33 (1.06–1.66) 1.00 (0.81–1.23) 1.05 (0.77–1.42) 
Smad3 MAD3 15q22.33 rs750766 G/A 0.48 0.98 (0.84–1.15) 0.93 (0.77–1.13) 1.09 (0.89–1.34)b  
 MADH3  rs893473 C/T 0.17 0.97 (0.84–1.13) 0.99 (0.69–1.40)  1.09 (0.73–1.62)c 
 JV15-2  rs991157 G/A 0.3  0.93 (0.73–1.18)c  1.11 (0.79–1.55)c 
   rs1498506 A/C 0.48 0.87 (0.75–1.02) 0.69 (0.57–0.84) 1.10 (0.88–1.38) 0.96 (0.72–1.26) 
   rs1866317 C/G 0.11 0.98 (0.83–1.16) 0.92 (0.48–1.76) 1.12 (0.88–1.42) 1.65 (0.77–3.54) 
   rs2118610 G/A 0.45 1.11 (0.95–1.29) 0.94 (0.78–1.14) 1.02 (0.82–1.27) 1.02 (0.77–1.36) 
   rs2118611 A/G 0.2 1.03 (0.90–1.19)b  0.89 (0.73–1.09)b  
   rs2414937 G/C 0.2  0.68 (0.47–0.97)c 1.01 (0.82–1.24) 1.02 (0.63–1.67) 
   rs3743343 T/C 0.24 1.10 (0.95–1.26) 1.15 (0.87–1.52) 0.97 (0.79–1.19) 1.10 (0.77–1.58) 
   rs3825977 C/T 0.19 0.95 (0.82–1.11) 1.01 (0.72–1.42) 0.96 (0.78–1.18) 0.76 (0.47–1.24) 
   rs4147358 C/A 0.22 1.08 (0.93–1.24) 0.99 (0.73–1.33) 1.03 (0.84–1.27) 0.89 (0.60–1.33) 
   rs4776892 A/T 0.18 1.02 (0.89–1.18)b  0.94 (0.76–1.16) 1.14 (0.69–1.88) 
   rs7163381 G/A 0.26  0.76 (0.59–0.98)c  1.11 (0.79–1.56)c 
   rs7176870 A/G 0.43 1.08 (0.93–1.24)b  1.16 (0.94–1.42)b  
   rs11071933 C/G 0.33 1.04 (0.90–1.20) 0.94 (0.75–1.16) 0.84 (0.69–1.03)b  
   rs12901071 A/G 0.34  0.68 (0.55–0.85)c  0.80 (0.57–1.10)c 
   rs17293443 T/C 0.22  0.84 (0.60–1.18)c  1.81 (1.12–2.91)c 
Smad4 DPC4 18q21.1 rs10502913 G/A 0.24 1.03 (0.89–1.19) 0.81 (0.61–1.08) 1.02 (0.83–1.25) 1.09 (0.72–1.67) 
 MADH4         
TGFβ1 TGFB 19q13.1 rs1800469 G/A 0.31 0.89 (0.78–1.03) 0.65 (0.51–0.84) 1.02 (0.84–1.23)b  
   rs4803455 C/A 0.48 1.25 (1.06–1.47) 1.43 (1.18–1.73) 1.06 (0.84–1.32) 1.04 (0.79–1.35) 
TGFβR1 ALK-5 9q22 rs1571590 A/G 0.2 0.95 (0.82–1.10) 1.39 (0.98–1.96) 0.91 (0.74–1.12) 1.42 (0.85–2.39) 
 SKR4  rs6478974 T/A 0.49 0.85 (0.74–0.99)b  0.85 (0.69–1.05)b  
 LDS1A  rs10733710 G/A 0.2 1.07 (0.93–1.23)b  1.17 (0.96–1.43)b  
 AAT5         
ColonRectal
GeneAliasLocationSNPMajor/Minor alleleMAFaHeterozygote OR (95% CI)Homozygote rare OR (95% CI)Heterozygote OR (95% CI)Homozygote rare OR (95% CI)
Smad2 MAD2 18q21.1 rs1787199 A/T 0.46 1.08 (0.92–1.26) 1.24 (1.03–1.51) 0.85 (0.69–1.05)b  
 MADH2  rs1792689 C/T 0.13 0.96 (0.82–1.12) 1.30 (0.79–2.13) 0.78 (0.62–0.98)b  
 JV18  rs4940086 T/C 0.33 1.08 (0.94–1.25) 1.33 (1.06–1.66) 1.00 (0.81–1.23) 1.05 (0.77–1.42) 
Smad3 MAD3 15q22.33 rs750766 G/A 0.48 0.98 (0.84–1.15) 0.93 (0.77–1.13) 1.09 (0.89–1.34)b  
 MADH3  rs893473 C/T 0.17 0.97 (0.84–1.13) 0.99 (0.69–1.40)  1.09 (0.73–1.62)c 
 JV15-2  rs991157 G/A 0.3  0.93 (0.73–1.18)c  1.11 (0.79–1.55)c 
   rs1498506 A/C 0.48 0.87 (0.75–1.02) 0.69 (0.57–0.84) 1.10 (0.88–1.38) 0.96 (0.72–1.26) 
   rs1866317 C/G 0.11 0.98 (0.83–1.16) 0.92 (0.48–1.76) 1.12 (0.88–1.42) 1.65 (0.77–3.54) 
   rs2118610 G/A 0.45 1.11 (0.95–1.29) 0.94 (0.78–1.14) 1.02 (0.82–1.27) 1.02 (0.77–1.36) 
   rs2118611 A/G 0.2 1.03 (0.90–1.19)b  0.89 (0.73–1.09)b  
   rs2414937 G/C 0.2  0.68 (0.47–0.97)c 1.01 (0.82–1.24) 1.02 (0.63–1.67) 
   rs3743343 T/C 0.24 1.10 (0.95–1.26) 1.15 (0.87–1.52) 0.97 (0.79–1.19) 1.10 (0.77–1.58) 
   rs3825977 C/T 0.19 0.95 (0.82–1.11) 1.01 (0.72–1.42) 0.96 (0.78–1.18) 0.76 (0.47–1.24) 
   rs4147358 C/A 0.22 1.08 (0.93–1.24) 0.99 (0.73–1.33) 1.03 (0.84–1.27) 0.89 (0.60–1.33) 
   rs4776892 A/T 0.18 1.02 (0.89–1.18)b  0.94 (0.76–1.16) 1.14 (0.69–1.88) 
   rs7163381 G/A 0.26  0.76 (0.59–0.98)c  1.11 (0.79–1.56)c 
   rs7176870 A/G 0.43 1.08 (0.93–1.24)b  1.16 (0.94–1.42)b  
   rs11071933 C/G 0.33 1.04 (0.90–1.20) 0.94 (0.75–1.16) 0.84 (0.69–1.03)b  
   rs12901071 A/G 0.34  0.68 (0.55–0.85)c  0.80 (0.57–1.10)c 
   rs17293443 T/C 0.22  0.84 (0.60–1.18)c  1.81 (1.12–2.91)c 
Smad4 DPC4 18q21.1 rs10502913 G/A 0.24 1.03 (0.89–1.19) 0.81 (0.61–1.08) 1.02 (0.83–1.25) 1.09 (0.72–1.67) 
 MADH4         
TGFβ1 TGFB 19q13.1 rs1800469 G/A 0.31 0.89 (0.78–1.03) 0.65 (0.51–0.84) 1.02 (0.84–1.23)b  
   rs4803455 C/A 0.48 1.25 (1.06–1.47) 1.43 (1.18–1.73) 1.06 (0.84–1.32) 1.04 (0.79–1.35) 
TGFβR1 ALK-5 9q22 rs1571590 A/G 0.2 0.95 (0.82–1.10) 1.39 (0.98–1.96) 0.91 (0.74–1.12) 1.42 (0.85–2.39) 
 SKR4  rs6478974 T/A 0.49 0.85 (0.74–0.99)b  0.85 (0.69–1.05)b  
 LDS1A  rs10733710 G/A 0.2 1.07 (0.93–1.23)b  1.17 (0.96–1.43)b  
 AAT5         

aMAF based on White control population.

bDominant model.

cRecessive model.

The following associations were statistically significant for rectal cancer (Figure 1): OR = 0.78 (95% CI = 0.62–0.98) for CT/TT versus CC for Smad2 rs1792689; and OR = 1.81 (95% CI = 1.12–2.91) for CC versus TT/TC for Smad3 rs17293443. Although Smad3 rs11071933 and rs1866317 were not statistically significant independently, after adjusting for rs17293443 and one another, risk estimates were 0.75 (95% CI = 0.61–0.93 and 1.28 (95% CI = 1.03–1.59) for the CG/GG versus CC genotypes, respectively.

For colon cancer, we observed a statistically significant interaction between Smad3 rs3825977 and TGFβ1 rs1800469 and between Smad2 rs4940086, Smad3 rs17293443, and Smad7 rs4939827 with TGFβ1 rs4803455 (Table 2). Statistically significant interactions also were observed between both TGFBR1 rs6478974 and rs1571590 with IKBkB rs37473811 and with NFκB1 rs4648110 (Table 2). Statistically significant gene–gene interactions also were identified for rectal cancer (Table 3). TGFβ1 rs1800469 interacted with Smad3 rs211860 and rs4147358 (Table 3); TGFβ1 rs4803455 and TGFβR1 rs105733710 interacted with NFκB1 rs4648110 and rs13117745; TGFβR1 rs1571590 interacted significantly with Smad2 rs1792689.

Table 2.

Interaction between variants in the TGF-β signaling pathway and NFκB1 and IKBκB and risk of colon cancera

Controls, nCases, nOR95% CIControls, nCases, nOR95% CIControls, nCases, nOR95% CI
  TGFβ1 rs1800469 
 GG GA AA 
Smad3 rs3825977             
 CC 610 521 1.00  542 419 0.92 0.77 1.09 116 80 0.80 0.58 1.08 
 CT 277 230 0.99 0.80 1.23 259 201 0.90 0.72 1.12 68 35 0.57 0.37 0.88 
 TT 30 43 1.67 1.03 2.70 35 19 0.64 0.36 1.14 13 0.17 0.04 0.75 
Pinteraction   <0.01          
  TGFβ1 rs4803455 
 CC CA AA 
Smad2 rs4940086             
 TT 232 152 1.00  465 360 1.21 0.94–1.55 207 170 1.27 0.95–1.70 
 TC 228 162 1.10 0.83–1.47 425 335 1.24 0.96–1.59 196 180 1.46 1.09–1.95 
 CC 58 26 0.71 0.43–1.18 105 113 1.71 1.22–2.39 29 50 2.72 1.64–4.49 
Pinteraction   0.02          
Smad3 rs17293443             
 TT/TC 485 330 1.00  947 779 1.23 1.04–1.45 420 380 1.35 1.11–1.64 
 CC 33 10 0.46 0.22–0.94 48 29 0.93 0.57–1.51 11 20 2.92 1.38–6.19 
Pinteraction   <0.01          
Smad7 rs4939827             
 TT 115 106 1.00  255 225 0.99 0.72–1.37 123 110 0.99 0.69–1.44 
 TC/CC 403 233 0.63 0.46–0.86 738 582 0.87 0.65–1.16 309 290 1.04 0.76–1.42 
Pinteraction   0.02          
  TGFβR1 rs6478974 
 CC CA AA 
IKBκB rs3747811             
 TT 148 155 1.00  255 208 0.78 0.58–1.05 124 84 0.66 0.46–0.95 
 TA 270 219 0.78 0.58–1.04 472 352 0.72 0.55–0.95 250 171 0.67 0.49–0.90 
 AA 115 104 0.87 0.61–1.24 239 171 0.69 0.51–0.94 83 90 1.04 0.71–1.52 
Pinteraction   0.04          
NFκB1 rs4648110             
 TT 346 289 1.00  615 474 0.93 0.76–1.14 282 233 1.01 0.80–1.28 
 TA 163 175 1.29 0.99–1.68 311 234 0.91 0.72–1.15 156 105 0.82 0.61–1.10 
 AA 24 14 0.71 0.36–1.40 40 23 0.69 0.40–1.18 19 0.47 0.20–1.15 
Pinteraction   0.04          
  TGFβR1 rs1571590 
 AA AG GG 
IKBκB rs3747811b             
TT 347 268 1.00  166 156 1.24 0.94–1.62 14 23 2.24 1.13–4.44 
TA 640 500 1.03 0.85–1.26 317 209 0.86 0.68–1.09 35 33 1.25 0.75–2.06 
AA 273 239 1.14 0.90–1.44 147 111 1.01 0.75–1.36 17 16 1.24 0.61–2.51 
Pinteraction   0.04          
Controls, nCases, nOR95% CIControls, nCases, nOR95% CIControls, nCases, nOR95% CI
  TGFβ1 rs1800469 
 GG GA AA 
Smad3 rs3825977             
 CC 610 521 1.00  542 419 0.92 0.77 1.09 116 80 0.80 0.58 1.08 
 CT 277 230 0.99 0.80 1.23 259 201 0.90 0.72 1.12 68 35 0.57 0.37 0.88 
 TT 30 43 1.67 1.03 2.70 35 19 0.64 0.36 1.14 13 0.17 0.04 0.75 
Pinteraction   <0.01          
  TGFβ1 rs4803455 
 CC CA AA 
Smad2 rs4940086             
 TT 232 152 1.00  465 360 1.21 0.94–1.55 207 170 1.27 0.95–1.70 
 TC 228 162 1.10 0.83–1.47 425 335 1.24 0.96–1.59 196 180 1.46 1.09–1.95 
 CC 58 26 0.71 0.43–1.18 105 113 1.71 1.22–2.39 29 50 2.72 1.64–4.49 
Pinteraction   0.02          
Smad3 rs17293443             
 TT/TC 485 330 1.00  947 779 1.23 1.04–1.45 420 380 1.35 1.11–1.64 
 CC 33 10 0.46 0.22–0.94 48 29 0.93 0.57–1.51 11 20 2.92 1.38–6.19 
Pinteraction   <0.01          
Smad7 rs4939827             
 TT 115 106 1.00  255 225 0.99 0.72–1.37 123 110 0.99 0.69–1.44 
 TC/CC 403 233 0.63 0.46–0.86 738 582 0.87 0.65–1.16 309 290 1.04 0.76–1.42 
Pinteraction   0.02          
  TGFβR1 rs6478974 
 CC CA AA 
IKBκB rs3747811             
 TT 148 155 1.00  255 208 0.78 0.58–1.05 124 84 0.66 0.46–0.95 
 TA 270 219 0.78 0.58–1.04 472 352 0.72 0.55–0.95 250 171 0.67 0.49–0.90 
 AA 115 104 0.87 0.61–1.24 239 171 0.69 0.51–0.94 83 90 1.04 0.71–1.52 
Pinteraction   0.04          
NFκB1 rs4648110             
 TT 346 289 1.00  615 474 0.93 0.76–1.14 282 233 1.01 0.80–1.28 
 TA 163 175 1.29 0.99–1.68 311 234 0.91 0.72–1.15 156 105 0.82 0.61–1.10 
 AA 24 14 0.71 0.36–1.40 40 23 0.69 0.40–1.18 19 0.47 0.20–1.15 
Pinteraction   0.04          
  TGFβR1 rs1571590 
 AA AG GG 
IKBκB rs3747811b             
TT 347 268 1.00  166 156 1.24 0.94–1.62 14 23 2.24 1.13–4.44 
TA 640 500 1.03 0.85–1.26 317 209 0.86 0.68–1.09 35 33 1.25 0.75–2.06 
AA 273 239 1.14 0.90–1.44 147 111 1.01 0.75–1.36 17 16 1.24 0.61–2.51 
Pinteraction   0.04          

aAssociations adjusted for age, sex, center and race.

bSimilar associations were observed for NFκB1 rs13117745 (C > T), Pinteraction = 0.02.

Table 3.

Associations between variants in the TGF-β signaling pathway and NFκB1 and Smad2 and Smad3 and rectal cancer riska

Controls, nCases, nOR95% CIControls, nCases, nOR95% CIControls, nCases, nOR95% CI
  TGFβ1 rs1800469 
 GG GA AA 
Smad3 rs2118610b             
 GG 158 119 1.00  140 104 0.99 0.70–1.40 23 35 1.95 1.09–3.48 
 GA 224 166 1.03 0.75–1.40 178 156 1.18 0.86–1.63 52 35 0.91 0.56–1.49 
 AA 83 72 1.22 0.82–1.81 76 61 1.11 0.73–1.68 19 0.29 0.10–0.88 
Pinteraction   0.01          
Smad3 rs4147358c             
 CC 254 204 1.00  220 181 0.76–1.31 63 31 0.58 0.36–0.93 
 CA 184 137 0.89 0.89–1.19 138 115 0.99 0.72–1.35 26 34 1.59 0.92–2.74 
 AA 27 16 0.67 0.35–1.28 36 25 0.83 0.48–1.43 1.96 0.64–6.03 
Pinteraction   <0.01          
  TGFβ1 rs4803455 
 CC CA AA 
NFκB1 rs13117745d             
 CC 201 130 1.00  339 274 1.25 0.95–1.65 155 132 1.35 0.98–1.86 
 CT/TT 52 66 2.00 1.30–3.06 142 105 1.18 0.84–1.65 69 44 1.00 0.64–1.55 
Pinteraction   <0.01          
  TGFβR1 rs10733710 
 GG GA/AA     
NFκB1 rs4648110e             
 TT 405 270 1.00  207 201 1.45 1.13–1.86     
 TA/AA 210 184 1.33 1.04–1.72 136 98 1.08 0.80–1.47     
Pinteraction   <0.01          
  TGFβR1 rs1571590 
 AA AG GG 
Smad2 rs1792689             
 CC 455 379 1.00  241 183 0.91 0.72–1.16 15 28 2.42 1.27–4.61 
 CT/TT 156 112 0.84 0.63–1.11 78 48 0.75 0.51–1.11 14 0.32 0.10–0.99 
Pinteraction   0.003          
Controls, nCases, nOR95% CIControls, nCases, nOR95% CIControls, nCases, nOR95% CI
  TGFβ1 rs1800469 
 GG GA AA 
Smad3 rs2118610b             
 GG 158 119 1.00  140 104 0.99 0.70–1.40 23 35 1.95 1.09–3.48 
 GA 224 166 1.03 0.75–1.40 178 156 1.18 0.86–1.63 52 35 0.91 0.56–1.49 
 AA 83 72 1.22 0.82–1.81 76 61 1.11 0.73–1.68 19 0.29 0.10–0.88 
Pinteraction   0.01          
Smad3 rs4147358c             
 CC 254 204 1.00  220 181 0.76–1.31 63 31 0.58 0.36–0.93 
 CA 184 137 0.89 0.89–1.19 138 115 0.99 0.72–1.35 26 34 1.59 0.92–2.74 
 AA 27 16 0.67 0.35–1.28 36 25 0.83 0.48–1.43 1.96 0.64–6.03 
Pinteraction   <0.01          
  TGFβ1 rs4803455 
 CC CA AA 
NFκB1 rs13117745d             
 CC 201 130 1.00  339 274 1.25 0.95–1.65 155 132 1.35 0.98–1.86 
 CT/TT 52 66 2.00 1.30–3.06 142 105 1.18 0.84–1.65 69 44 1.00 0.64–1.55 
Pinteraction   <0.01          
  TGFβR1 rs10733710 
 GG GA/AA     
NFκB1 rs4648110e             
 TT 405 270 1.00  207 201 1.45 1.13–1.86     
 TA/AA 210 184 1.33 1.04–1.72 136 98 1.08 0.80–1.47     
Pinteraction   <0.01          
  TGFβR1 rs1571590 
 AA AG GG 
Smad2 rs1792689             
 CC 455 379 1.00  241 183 0.91 0.72–1.16 15 28 2.42 1.27–4.61 
 CT/TT 156 112 0.84 0.63–1.11 78 48 0.75 0.51–1.11 14 0.32 0.10–0.99 
Pinteraction   0.003          

aAssociation adjusted for age, sex, race and center.

bSimilar associations were observed for SMAD3 rs991157 (G > A), Pinteraction < 0.01.

cSimilar associations were observed for SMAD3 rs745103 (T > A), Pinteraction < 0.01.

dSimilar associations were observed for NFκB1 rs4648110 (T > A), Pinteraction = 0.02.

dSimilar associations were observed for NFκB1 rs13117745 (C > T), Pinteraction = 0.01.

Several variants within the TGF-β signaling pathway interacted with lifestyle factors hypothesized as influencing this pathway. Statistically significant interactions with cigarette smoking and colon cancer were observed for TGFβ1 rs4803455, TGFβR1 10733710, and rs1571590 (Table 4). As previously noted, the AA genotype of TGFβ1 rs4803455 increased risk of colon cancer overall, but the increase in risk was especially dramatic among recent smokers (OR = 2.09, 95% CI = 1.47–2.96). The GG genotype of TGFβR1 rs1571590 was associated with increased colon cancer risk among nonsmokers/former smokers while there was a trend toward reduced risk among recent cigarette smokers for the same genotype. The A allele of TGFβ1 rs1800469 was observed as increasing rectal cancer risk among recent smokers.

Table 4.

Interaction between genetic variants in the TGF-β signaling pathway and lifestyle factors and risk of colon and rectal cancer

Controls, nCases, nORa95% CIControls, nCases, nORa95% CI
Colon cancer Never smoker/Former smoker Recent smoker 
TGFβ1 rs4803455         
 CC 422 274 1.00  96 66 1.04 0.74–1.48 
 CA 815 650 1.25 1.04–1.50 180 155 1.31 1.01–1.71 
 AA 363 306 1.33 1.07–1.65 68 94 2.09 1.47–2.96 
Pinteraction   0.05      
TGFβR1 rs10733710         
 GG 1,004 775 1.00  223 178 0.99 0.79–1.24 
 GA/AA 586 452 0.99 0.85–1.16 120 138 1.46 1.12–1.90 
Pinteraction   0.03      
TGFβR1 rs1571590         
 AA 1,053 797 1.00  206 207 1.29 1.04–1.60 
 AG 505 376 0.99 0.84–1.17 125 101 1.04 (0.78–1.37) 
 GG 51 62 1.63 1.10–2.37 15 10 0.88 0.39–1.98 
Pinteraction   0.05      
Rectal cancer         
TGFβ1 rs1800469         
 GG 385 295 1.00  80 61 0.97 0.67–1.40 
 GA/AA 419 305 0.93 0.75–1.15 69 87 1.58 1.11–2.24 
Pinteraction   0.03      
Colon cancer No recent aspirin/NSAID use Recent aspirin NSAID use 
TGFβR1 rs6478974         
 TT 329 313 1.00  202 160 0.83 0.64–1.07 
 TA 554 502 0.95 0.78–1.16 401 223 0.59 0.47–0.74 
 AA 253 238 1.01 0.79–1.28 201 101 0.54 0.40–0.71 
Pinteraction   0.03      
Smad3 rs3743343         
 TT 663 567 1.00  462 291 0.73 0.61–0.88 
 TC 402 401 1.15 0.96–1.38 295 177 0.70 0.56–0.87 
 CC 70 85 1.43 1.02–2.00 47 18 0.45 0.26–0.78 
Pinteraction   0.02      
Smad3 rs7173811         
 CC 323 263 1.00  228 147 0.79 0.61–1.03 
 CT 549 520 1.15 0.94–1.41 378 235 0.77 0.61–0.96 
 TT 264 270 1.24 0.98–1.57 198 104 0.63 0.47–0.85 
Pinteraction   0.03      
Rectal cancer         
Smad3 rs3743343         
 TT 272 268 1.00  245 137 0.57 0.44–0.75 
 TC 205 173 0.84 0.65–1.10 156 105 0.69 (0.51–0.93) 
 CC 44 36 0.81 0.50–1.30 27 29 1.03 0.59–1.80 
Pinteraction   0.01      
Smad3 rs7163381c         
 GG 268 229 1.00  206 151 0.87 0.66–1.14 
 GA 219 198 1.04 0.80–1.35 176 96 0.63 0.46–0.86 
 AA 34 50 1.65 1.02–2.67 46 24 0.58 0.34–0.99 
Pinteraction   0.01      
Colon cancer No recent estrogen exposure Recent estrogen exposure 
TGFβ1 rs1800469         
 GG 253 209 1.00  653 579 0.78 0.59–1.03 
 GA 213 200 1.16 0.89–1.51 612 433 0.62 0.47–0.82 
 AA 53 39 0.85 0.54–1.34 141 77 0.47 0.32–0.68 
Pinteraction   0.03      
Rectal cancer         
TGFβ1 rs4803455         
 CC 40 40 1.00  213 155 0.53 0.32–0.90 
 CA 84 76 0.89 0.52–1.53 397 303 0.57 0.34–0.94 
 AA 45 25 0.54 0.28–1.05 179 151 0.64 0.38–1.08 
Pinteraction   0.04      
Smad4 rs10502913c Men Women 
 GG 318 245 1.00  248 197 1.04 0.81–1.33 
 GA 196 174 1.17 0.90–1.53 144 94 0.86 0.63–1.17 
 AA 26 32 1.57 0.91–2.72 25 12 0.64 0.31–1.30 
Pinteraction   0.02      
Controls, nCases, nORa95% CIControls, nCases, nORa95% CI
Colon cancer Never smoker/Former smoker Recent smoker 
TGFβ1 rs4803455         
 CC 422 274 1.00  96 66 1.04 0.74–1.48 
 CA 815 650 1.25 1.04–1.50 180 155 1.31 1.01–1.71 
 AA 363 306 1.33 1.07–1.65 68 94 2.09 1.47–2.96 
Pinteraction   0.05      
TGFβR1 rs10733710         
 GG 1,004 775 1.00  223 178 0.99 0.79–1.24 
 GA/AA 586 452 0.99 0.85–1.16 120 138 1.46 1.12–1.90 
Pinteraction   0.03      
TGFβR1 rs1571590         
 AA 1,053 797 1.00  206 207 1.29 1.04–1.60 
 AG 505 376 0.99 0.84–1.17 125 101 1.04 (0.78–1.37) 
 GG 51 62 1.63 1.10–2.37 15 10 0.88 0.39–1.98 
Pinteraction   0.05      
Rectal cancer         
TGFβ1 rs1800469         
 GG 385 295 1.00  80 61 0.97 0.67–1.40 
 GA/AA 419 305 0.93 0.75–1.15 69 87 1.58 1.11–2.24 
Pinteraction   0.03      
Colon cancer No recent aspirin/NSAID use Recent aspirin NSAID use 
TGFβR1 rs6478974         
 TT 329 313 1.00  202 160 0.83 0.64–1.07 
 TA 554 502 0.95 0.78–1.16 401 223 0.59 0.47–0.74 
 AA 253 238 1.01 0.79–1.28 201 101 0.54 0.40–0.71 
Pinteraction   0.03      
Smad3 rs3743343         
 TT 663 567 1.00  462 291 0.73 0.61–0.88 
 TC 402 401 1.15 0.96–1.38 295 177 0.70 0.56–0.87 
 CC 70 85 1.43 1.02–2.00 47 18 0.45 0.26–0.78 
Pinteraction   0.02      
Smad3 rs7173811         
 CC 323 263 1.00  228 147 0.79 0.61–1.03 
 CT 549 520 1.15 0.94–1.41 378 235 0.77 0.61–0.96 
 TT 264 270 1.24 0.98–1.57 198 104 0.63 0.47–0.85 
Pinteraction   0.03      
Rectal cancer         
Smad3 rs3743343         
 TT 272 268 1.00  245 137 0.57 0.44–0.75 
 TC 205 173 0.84 0.65–1.10 156 105 0.69 (0.51–0.93) 
 CC 44 36 0.81 0.50–1.30 27 29 1.03 0.59–1.80 
Pinteraction   0.01      
Smad3 rs7163381c         
 GG 268 229 1.00  206 151 0.87 0.66–1.14 
 GA 219 198 1.04 0.80–1.35 176 96 0.63 0.46–0.86 
 AA 34 50 1.65 1.02–2.67 46 24 0.58 0.34–0.99 
Pinteraction   0.01      
Colon cancer No recent estrogen exposure Recent estrogen exposure 
TGFβ1 rs1800469         
 GG 253 209 1.00  653 579 0.78 0.59–1.03 
 GA 213 200 1.16 0.89–1.51 612 433 0.62 0.47–0.82 
 AA 53 39 0.85 0.54–1.34 141 77 0.47 0.32–0.68 
Pinteraction   0.03      
Rectal cancer         
TGFβ1 rs4803455         
 CC 40 40 1.00  213 155 0.53 0.32–0.90 
 CA 84 76 0.89 0.52–1.53 397 303 0.57 0.34–0.94 
 AA 45 25 0.54 0.28–1.05 179 151 0.64 0.38–1.08 
Pinteraction   0.04      
Smad4 rs10502913c Men Women 
 GG 318 245 1.00  248 197 1.04 0.81–1.33 
 GA 196 174 1.17 0.90–1.53 144 94 0.86 0.63–1.17 
 AA 26 32 1.57 0.91–2.72 25 12 0.64 0.31–1.30 
Pinteraction   0.02      

aAdjusted for age, center, race, and sex.

bSimilar association observed for Smad3 rs11071933; Pinteraction = 0.03.

cSimilar associations observed for Smad4 rs8096092; Pinteraction = 0.02.

The TGFβR1 rs6478974 A allele was associated with reduced risk of colon cancer among those who recently used aspirin/NSAIDs and had no effect among non–aspirin/NSAID users (Table 4). Smad3 rs3743343 interacted significantly with aspirin/NSAIDs for both colon and rectal cancers, although the direction of the association was different for these cancer sites. Statistically significant interactions were observed for Smad3 rs7173811 and aspirin/NSAIDs for colon cancer and both Smad3 rs7163381 and rs11071933 and rectal cancer. Among these SNPs, those who had the variant allele were at increased risk if they did not use aspirin/NSAIDs regularly but were at significantly reduced risk if they used aspirin/NSAIDs regularly.

Among women recently exposed to estrogen, the A allele of TGFβ1 rs1800469 was associated with a reduced risk of colon cancer and the C allele of rs4803455 was associated with a decreased risk of rectal cancer (Table 4). Likewise, both variants of Smad4, rs10502913 and rs8096092, were associated with increased risk of rectal cancer among men while reducing risk among women.

Unique sets of Smad2, Smad3, TGFβ1, and TGFβR1 SNPs were associated with tumor phenotypes for colon and rectal cancer (Table 5). Among colon cancer cases, the risk of a CIMP+ tumor was associated with both Smad2 and Smad3. TGFβ1 rs1800469 was associated with a decreased risk for all colon tumor phenotypes except CIMP+, although not associated with rectal molecular phenotype. TP53-mutated colon tumors were associated with Smad2 rs4940086 and Smad3 rs7176870. MSI+ colon tumors were associated with Smad2 rs1792689 and rs1787199 and Smad3 rs12901071 and rs731874. For rectal cancer, Smad3 rs893473 was associated with an increased likelihood of a CIMP+ tumor (OR = 3.6, 95% CI = 1.62–798) for the TT genotype relative to CC/CT; rs991157 AA versus GG/GA was associated with a statistically significant increased risk of a KRAS2-mutated tumor (OR = 1.63, 95% CI = 1.03–2.79). The TGFβR1 rs10733710 GA/AA genotype was associated with increased risk for both CIMP+ tumors and TP53-mutated tumors.

Table 5.

Associations between tumor molecular phenotype and TGFβ and Smad genes

Controls, nCases, nORa95% CI
Colon tumors  CIMP+ 
Smad2 rs1787199 AA 601 64 1.00  
 Note: Similar results for rs4940086 AT/TT 1,355 208 1.46 1.09–1.97 
Smad3 rs2118611b AA 1,226 152 1.00  
 AG/GG 729 120 1.87 1.26–2.79 
Smad3 rs4776892 AA 1,288 175 1.00  
 AT/TT 667 97 0.63 0.42–0.95 
  KRAS2 mutation 
TGFβ1 rs4803455 CC 526 74 1.00  
 CA/AA 1,457 280 1.40 1.06–1.85 
TGFβ1 rs1800469 GG 932 187 1.00  
 GA/AA 1,046 166 0.78 0.62–0.98 
  TP53 mutation 
Smad2 rs4940086 TT/TC 1,762 449 1.00  
 CC 194 67 1.38 1.02–1.86 
Smad3 rs7176870 AA 644 146 1.00  
 AG/GG 1,311 371 1.28 1.03–1.59 
TGFβ1 rs4803455 CC 526 111 1.00  
 CA 1,014 267 1.27 0.99–1.63 
 AA 443 144 1.56 1.18–2.07 
TGFβ1 rs1800469 GG 932 275 1.00  
 GA/AA 1,046 243 0.78 0.64–0.95 
  MSI unstable 
Smad2 rs1792689 CC 1,477 132 1.00  
 Note: Similar results for rs1787199 CT 448 45 1.12 0.79–1.60 
 TT 31 2.85 1.28–6.36 
Smad3 rs12901071b AA/AG 1,716 174 1.00  
 Note: Similar results for rs731874 GG 240 11 0.43 0.23–0.83 
TGFβ1 rs1800469 GG 932 110 1.00  
 GA/AA 1,046 80 0.64 0.47–0.86 
Rectal tumors  CIMP+ 
Smad3 rs893473 CC/CT 899 49 1.00  
 TT 60 10 3.60 1.62–7.98 
TGFβR1 rs10733710 GG 615 27 1.00  
 GA/AA 343 32 2.10 1.24–3.57 
  KRAS2 mutation 
Smad3 (rs991157) GG/GA 876 150 1.00  
 AA 83 23 1.69 1.03–2.79 
  TP53 mutation 
Smad3 rs11071933b CC 385 127 1.00  
 CG/GG 572 150 0.72 0.54–0.95 
Smad3 rs750766 GG 304 70 1.00  
 Note: Similar results for rs12102171 and rs7176870 GA/AA 653 207 1.49 1.09–2.04 
TGFβR1 rs10733710 GG 615 155 1.00  
 GA/AA 343 105 1.40 1.06–1.84 
Controls, nCases, nORa95% CI
Colon tumors  CIMP+ 
Smad2 rs1787199 AA 601 64 1.00  
 Note: Similar results for rs4940086 AT/TT 1,355 208 1.46 1.09–1.97 
Smad3 rs2118611b AA 1,226 152 1.00  
 AG/GG 729 120 1.87 1.26–2.79 
Smad3 rs4776892 AA 1,288 175 1.00  
 AT/TT 667 97 0.63 0.42–0.95 
  KRAS2 mutation 
TGFβ1 rs4803455 CC 526 74 1.00  
 CA/AA 1,457 280 1.40 1.06–1.85 
TGFβ1 rs1800469 GG 932 187 1.00  
 GA/AA 1,046 166 0.78 0.62–0.98 
  TP53 mutation 
Smad2 rs4940086 TT/TC 1,762 449 1.00  
 CC 194 67 1.38 1.02–1.86 
Smad3 rs7176870 AA 644 146 1.00  
 AG/GG 1,311 371 1.28 1.03–1.59 
TGFβ1 rs4803455 CC 526 111 1.00  
 CA 1,014 267 1.27 0.99–1.63 
 AA 443 144 1.56 1.18–2.07 
TGFβ1 rs1800469 GG 932 275 1.00  
 GA/AA 1,046 243 0.78 0.64–0.95 
  MSI unstable 
Smad2 rs1792689 CC 1,477 132 1.00  
 Note: Similar results for rs1787199 CT 448 45 1.12 0.79–1.60 
 TT 31 2.85 1.28–6.36 
Smad3 rs12901071b AA/AG 1,716 174 1.00  
 Note: Similar results for rs731874 GG 240 11 0.43 0.23–0.83 
TGFβ1 rs1800469 GG 932 110 1.00  
 GA/AA 1,046 80 0.64 0.47–0.86 
Rectal tumors  CIMP+ 
Smad3 rs893473 CC/CT 899 49 1.00  
 TT 60 10 3.60 1.62–7.98 
TGFβR1 rs10733710 GG 615 27 1.00  
 GA/AA 343 32 2.10 1.24–3.57 
  KRAS2 mutation 
Smad3 (rs991157) GG/GA 876 150 1.00  
 AA 83 23 1.69 1.03–2.79 
  TP53 mutation 
Smad3 rs11071933b CC 385 127 1.00  
 CG/GG 572 150 0.72 0.54–0.95 
Smad3 rs750766 GG 304 70 1.00  
 Note: Similar results for rs12102171 and rs7176870 GA/AA 653 207 1.49 1.09–2.04 
TGFβR1 rs10733710 GG 615 155 1.00  
 GA/AA 343 105 1.40 1.06–1.84 

aAdjusted for age, center, sex, and race.

btagSNPs presented for this gene are adjusted for one another.

Variations in TGFβ1, Smad1, Smad2, and Smad4 were not associated with survival after diagnosis (data not shown in table). Four SNPs were associated with colon cancer survival: TGFβR1 rs10733710 GA/AA versus GG (HRR = 0.73, 95% CI = 0.57–0.95); and 3 Smad3 SNPs, rs11639295 TT versus CC/CT (HRR = 0.46, 95% CI = 0.27–0.80); rs12708492 CT/TT versus CC (HRR = 1.78, 95% CI = 1.27–2.50), and rs2414937 CC versus GG (HRR = 2.54, 95% CI = 1.29–3.95). For rectal cancer, 4 SNPs also were associated with survival, although the associated SNPs were different from those that were associated with colon cancer. For rectal cancer, the associations were as follows: TGFβR1 rs6478974 AA versus TT genotype (HRR = 1.73, 95% CI = 1.08–2.78) and rs1571590 AG/GG versus AA genotype (HRR = 0.64, 95% CI = 0.43–0.95); Smad3 rs12904944 GA/AA versus GG (HRR = 1.45, 95% CI = 1.03–2.04) and rs3825977 CT/TT versus CC genotype (HRR = 1.55, 95% CI = 1.10–2.18).

The TGF-β signaling pathway is thought to play a critical role in the carcinogenic process because of its involvement in the regulation of cell growth, differentiation, proliferation, and apoptosis (23). TGF-β exerts its physiologic effect by activating its receptors. Once the TGF-β receptor complex is activated, intracellular signaling is initiated. The TGF-β receptor complex activates the Smad signaling pathway by directly phosphorlyating Smad2 and Smad3 that work in conjunction with Smad4 (24). In this study, genetic variation in TGFβ1 was associated with an increased risk of colon cancer, but not rectal cancer. Our evaluation of genetic variation in TGF-β signaling pathway showed several variants associated with colon and rectal cancer, acting independently as well as modifying the effect of other genetic and lifestyle factors.

A major function of TGF-β is mediating intracellular actions of proinflammatory cytokines, including activation of NFκB (2, 3). Deficiency of TGF-β has been shown to lead to extensive inflammation (2). Inflammation status of the gut seems to play a critical role in the etiology of both colon and rectal cancers (25). Our data support the role of TGF-β in an inflammation-related pathway, given the interaction between genetic variants of NFκB1 and TGFβ1 and TGFβR1 for both colon and rectal cancers. NFκB is an important nuclear transcription factor that regulates a large number of cytokines and is critical for the regulation of inflammation; increased transcription of NFκB can increase inflammation and angiogenesis as well as cell survival and growth (26). IkBκB is a key regulator of NFκB transcriptional activity (27); its proteins are inhibitors of NFκB (26). In addition to the interaction between other genes involved in the regulation of inflammation and variants in the TGF-β signaling pathway, we observed significant interaction with recent use of aspirin/NSAIDs and TGFβR1 rs6478974 and risk of colon cancer, further supporting an inflammation-related mechanism.

It has been hypothesized that cigarette smoking can influence inflammation via enhanced oxidative stress. Furthermore, cigarette smoke has been shown to regulate the effect of various cytokines, including TGF-β (27–30). We observed statistically significant interaction between TGFβ1 and TGFβR1 variants and cigarette smoke and colon cancer, thus supporting this link in a population-based study. We also observed statistically significant interaction between estrogen and TGFβ1 rs4803455. Estrogen has many physiologic properties and has been shown to influence both inflammation and insulin (31, 32).

One of the major mechanisms of TGF-β signaling is through a Smad-dependent pathway (6); Smad7 promotes the anti-inflammatory action of the TGF-β signaling pathway (6). Thus, we evaluated how genetic variants between TGFβ1 and TGFβR1 were associated with Smad2, Smad3, Smad4, and Smad7. We have previously reported on independent associations between Smad7 and colon cancer (8). In this article, we provide information on Smad2, Smad3, and Smad4, which have been hypothesized as important components of the TGF-β signaling pathway (34), as well as evaluate how Smad7 interacts with other genes in the pathway. Both Smad2 and Smad3 showed independent associations with colon cancer; however, several variants also showed consistent associations with CIMP+ tumors. Smad has been associated with epigenetic silencing in other cancers (35). Smad2 and Smad7 interacted significantly with TGFβ1 and TGFβR1, further supporting the importance of multiple elements of the TGF-β signaling pathway in the etiology of colon and rectal cancer.

Both TGFβR1 and Smad3 were associated with survival after diagnosis with colon and rectal cancer. We evaluated genetic variations in our candidate pathway because of its documented role in cell differentiation, metastasis, and survival (36–38). These associations were detected independent of stage at time of diagnosis and tumor characteristics. Although many SNPs were associated with survival, the ones of most importance often varied after diagnosis with colon versus rectal cancer. It is not readily clear why these differences were observed; however, many differences have been detected previously for colon and rectal cancer, suggesting different elements to their etiology and possible prognosis.

There are many strengths and limitations to this study. Others have evaluated polymorphisms in TGFβ1 with CRC and have found some associations with some polymorphisms (39, 40). In our study, we were able to thoroughly evaluate this candidate pathway, using both tagSNP and haplotype analysis, looking at colon and rectal cancers separately and evaluating associations that may be unique to certain tumor molecular phenotypes. The data are extensive and allow us to evaluate interactions with hypothesized genes as well as with hypothesized lifestyle factors. This approach has enabled us to acquire a more comprehensive understanding of the TGF-β signaling pathway and colon and rectal cancer. Although the candidate pathway and specific genes were hypothesize a priori as being associated with colon and rectal cancer, the process of a thorough evaluation lead to many comparisons. Replication of these findings in other studies is therefore needed.

Our data suggest that the TGF-β signaling pathway in conjunction with Smad is an important component of colon and rectal cancer risk and survival after diagnosis. Environmental factors, such as smoking cigarettes and using aspirin/NSAIDs, modulate this risk. Also of importance is the finding that some of these genes preferentially influenced the development of CIMP+ tumors, providing additional information on the carcinogenic process. Support for these findings from other similar studies is necessary to verify these associations.

Summary of All Genes and SNPs Assessed

GeneChromosome locationSNPRegionMAFMajor/Minor alleleFDR HWE probabilityColon homozygote are ORRectal homozygote rare OR
Smad1 4q31 rs714195 Intronic 0.42 G/A 0.73 0.99 (0.80–1.21) 1.02 (0.74–1.39) 
  rs6537355 5 upstream 0.12 A/G 0.88 1.35 (0.72–2.54) 0.93 (0.37–2.32) 
  rs2118438 Intronic 0.19 G/A 0.61 1.11 (0.75–1.65) 1.29 (0.72–2.34) 
  rs1016792 Intronic 0.19 T/C 1.00 1.00 (0.69–1.46) 0.90 (0.51–1.58) 
  rs12505085 3 downstream 0.23 A/G 0.89 0.88 (0.65–1.20) 0.91 (0.57–1.47) 
Smad2 18q21.1 rs1787199 Intronic 0.46 A/T 1.00 1.24 (1.03–1.51) 0.83 (0.63–1.08) 
  rs1792658 Intronic 0.21 A/C 0.96 1.18 (0.88–1.58) 1.12 (0.72–1.74) 
  rs1792689 Intronic 0.13 C/T 0.95 1.30 (0.79–2.13) 0.95 (0.41–2.22) 
  rs4940086 Intronic 0.33 T/C 1.00 1.33 (1.06–1.66) 1.05 (0.77–1.42) 
Smad3 15q22.33 rs731874 Intronic 0.28 G/A 1.00 1.06 (0.82–1.37) 0.94 (0.65–1.37) 
  rs745103 Intronic 0.45 T/C 0.86 0.96 (0.79–1.16) 0.98 (0.75–1.29) 
  rs750766 Unknown 0.48 G/A 1.00 0.93 (0.77–1.13) 1.06 (0.81–1.39) 
  rs893473 Intronic 0.17 C/T 1.00 0.99 (0.69–1.40) 1.06 (0.71–1.59) 
  rs991157 Intronic 0.30 G/A 1.00 0.96 (0.75–1.23) 1.07 (0.75–1.51) 
  rs1470003 Intronic 0.48 G/C 0.96 0.95 (0.79–1.15) 1.19 (0.90–1.57) 
  rs1498506 Intronic 0.48 A/C 1.00 0.69 (0.57–0.84) 0.96 (0.72–1.26) 
  rs1866317 Unknown 0.11 C/G 1.00 0.92 (0.48–1.76) 1.65 (0.77–3.54) 
  rs1992215 Unknown 0.33 T/C 1.00 1.00 (0.80–1.25) 0.86 (0.62–1.21) 
  rs2118610 Intronic 0.45 G/A 0.61 0.94 (0.78–1.14) 1.02 (0.77–1.36) 
  rs2118611 Intronic 0.20 A/G 0.99 0.94 (0.66–1.34) 0.93 (0.62–1.42) 
  rs2414937 Intronic 0.20 G/C 1.00 0.67 (0.47–0.97) 1.02 (0.63–1.67) 
  rs3743343 3 UTR 0.24 T/C 1.00 1.15 (0.87–1.52) 1.10 (0.77–1.58) 
  rs3784681 Intronic 0.29 G/C 0.96 0.91 (0.71–1.17) 0.79 (0.56–1.11) 
  rs3825977 Intronic 0.19 C/T 1.00 1.01 (0.72–1.42) 0.76 (0.47–1.24) 
  rs4147358 Intronic 0.22 C/A 0.96 0.99 (0.73–1.33) 0.89 (0.60–1.33) 
  rs4601989 Intronic 0.24 C/T 0.68 0.81 (0.60–1.08) 0.66 (0.44–1.00) 
  rs4776881 Intronic 0.44 T/C 1.00 1.07 (0.89–1.30) 1.21 (0.92–1.60) 
  rs4776890 Intronic 0.40 T/G 0.96 0.97 (0.80–1.19) 0.96 (0.72–1.28) 
  rs4776892 Intronic 0.18 A/T 0.45 1.00 (0.67–1.48) 1.14 (0.69–1.88) 
  rs7163381 Intronic 0.26 G/A 1.00 0.79 (0.61–1.03) 1.06 (0.74–1.51) 
  rs7173811 Intronic 0.47 C/T 0.96 1.06 (0.88–1.28) 0.96 (0.73–1.26) 
  rs7176870 Intronic 0.43 A/G 1.00 1.06 (0.88–1.29) 1.19 (0.90–1.58) 
  rs7181556 Intronic 0.24 C/T 0.99 0.91 (0.69–1.22) 0.75 (0.50–1.12) 
  rs7183244 Intronic 0.39 C/T 0.84 1.00 (0.81–1.23) 1.04 (0.77–1.41) 
  rs9972423 Intronic 0.37 T/A 1.00 0.97 (0.79–1.20) 1.27 (0.94–1.73) 
  rs11071933 Intronic 0.33 C/G 1.00 0.94 (0.75–1.16) 0.92 (0.68–1.25) 
  rs11637581 Intronic 0.28 C/T 0.95 0.98 (0.76–1.28) 1.11 (0.77–1.59) 
  rs11639295 Intronic 0.31 C/T 1.00 0.88 (0.70–1.12) 0.83 (0.59–1.16) 
  rs12102171 Intronic 0.17 C/T 0.86 0.90 (0.62–1.30) 0.84 (0.49–1.43) 
  rs12708492 Intronic 0.48 C/T 1.00 1.02 (0.85–1.24) 1.10 (0.84–1.44) 
  rs12901071 Intronic 0.34 A/G 0.68 0.67 (0.53–0.84) 0.85 (0.60–1.20) 
  rs12904944 Intronic 0.34 G/A 1.00 0.81 (0.64–1.01) 1.07 (0.79–1.47) 
  rs12907997 Intronic 0.50 C/T 1.00 0.95 (0.78–1.14) 0.91 (0.70–1.20) 
  rs12915039 Intronic 0.24 A/C 1.00 1.01 (0.76–1.35) 1.08 (0.72–1.61) 
  rs16950687 Intronic 0.28 A/G 1.00 0.93 (0.72–1.21) 1.22 (0.85–1.76) 
  rs17293443 Intronic 0.22 T/C 0.92 0.85 (0.61–1.19) 1.74 (1.08–2.82) 
Smad4 18q21.1 rs8096092 Intronic 0.38 C/A 0.68 1.00 (0.81–1.23) 1.17 (0.87–1.59) 
  rs10502913 Intronic 0.24 G/A 0.74 0.81 (0.61–1.08) 1.09 (0.72–1.67) 
Smad7 18q21.1 rs1316447 Intronic 0.19 C/T 1.00 0.86 (0.60–1.23) 0.88 (0.53–1.45) 
  rs2337106 Intronic 0.47 C/G 1.00 0.88 (0.72–1.06) 1.11 (0.85–1.45) 
  rs2337107 Intronic 0.41 G/A 0.99 1.12 (0.92–1.36) 0.97 (0.74–1.28) 
  rs3736242 Intronic 0.22 G/A 1.00 0.94 (0.69–1.28) 1.33 (0.84–2.09) 
  rs3764482 Intronic 0.19 C/T 1.00 1.25 (0.86–1.81) 0.60 (0.34–1.07) 
  rs4464148 Intronic 0.31 T/C 1.00 1.06 (0.83–1.35) 0.76 (0.54–1.09) 
  rs4939827 Intronic 0.49 T/C 1.00 0.79 (0.66–0.95) 0.95 (0.73–1.23) 
  rs4939832 Intronic 0.24 A/G 1.00 1.00 (0.76–1.32) 1.29 (0.87–1.92) 
  rs7238442 Intronic 0.46 T/C 0.82 1.12 (0.93–1.35) 0.92 (0.71–1.20) 
  rs12456328 Intronic 0.13 C/T 1.00 0.81 (0.49–1.33) 1.16 (0.51–2.66) 
  rs12953717 Intronic 0.42 C/T 1.00 1.36 (1.12–1.65) 0.90 (0.68–1.19) 
TGFβ1 19q13.1 rs1800469 5 upstream 0.31 G/A 1.00 0.65 (0.51–0.84) 0.98 (0.71–1.38) 
  rs4803455 Intronic 0.48 C/A 0.92 1.43 (1.18–1.73) 1.04 (0.79–1.35) 
TGFβR1 9q22 rs1571590 Intronic 0.20 A/G 0.67 1.39 (0.98–1.96) 1.42 (0.85–2.39) 
  rs6478974 Intronic 0.49 T/A 1.00 0.86 (0.71–1.04) 0.84 (0.63–1.10) 
  rs10733710 Intronic 0.20 G/A 0.96 1.06 (0.77–1.46) 1.22 (0.78–1.91) 
GeneChromosome locationSNPRegionMAFMajor/Minor alleleFDR HWE probabilityColon homozygote are ORRectal homozygote rare OR
Smad1 4q31 rs714195 Intronic 0.42 G/A 0.73 0.99 (0.80–1.21) 1.02 (0.74–1.39) 
  rs6537355 5 upstream 0.12 A/G 0.88 1.35 (0.72–2.54) 0.93 (0.37–2.32) 
  rs2118438 Intronic 0.19 G/A 0.61 1.11 (0.75–1.65) 1.29 (0.72–2.34) 
  rs1016792 Intronic 0.19 T/C 1.00 1.00 (0.69–1.46) 0.90 (0.51–1.58) 
  rs12505085 3 downstream 0.23 A/G 0.89 0.88 (0.65–1.20) 0.91 (0.57–1.47) 
Smad2 18q21.1 rs1787199 Intronic 0.46 A/T 1.00 1.24 (1.03–1.51) 0.83 (0.63–1.08) 
  rs1792658 Intronic 0.21 A/C 0.96 1.18 (0.88–1.58) 1.12 (0.72–1.74) 
  rs1792689 Intronic 0.13 C/T 0.95 1.30 (0.79–2.13) 0.95 (0.41–2.22) 
  rs4940086 Intronic 0.33 T/C 1.00 1.33 (1.06–1.66) 1.05 (0.77–1.42) 
Smad3 15q22.33 rs731874 Intronic 0.28 G/A 1.00 1.06 (0.82–1.37) 0.94 (0.65–1.37) 
  rs745103 Intronic 0.45 T/C 0.86 0.96 (0.79–1.16) 0.98 (0.75–1.29) 
  rs750766 Unknown 0.48 G/A 1.00 0.93 (0.77–1.13) 1.06 (0.81–1.39) 
  rs893473 Intronic 0.17 C/T 1.00 0.99 (0.69–1.40) 1.06 (0.71–1.59) 
  rs991157 Intronic 0.30 G/A 1.00 0.96 (0.75–1.23) 1.07 (0.75–1.51) 
  rs1470003 Intronic 0.48 G/C 0.96 0.95 (0.79–1.15) 1.19 (0.90–1.57) 
  rs1498506 Intronic 0.48 A/C 1.00 0.69 (0.57–0.84) 0.96 (0.72–1.26) 
  rs1866317 Unknown 0.11 C/G 1.00 0.92 (0.48–1.76) 1.65 (0.77–3.54) 
  rs1992215 Unknown 0.33 T/C 1.00 1.00 (0.80–1.25) 0.86 (0.62–1.21) 
  rs2118610 Intronic 0.45 G/A 0.61 0.94 (0.78–1.14) 1.02 (0.77–1.36) 
  rs2118611 Intronic 0.20 A/G 0.99 0.94 (0.66–1.34) 0.93 (0.62–1.42) 
  rs2414937 Intronic 0.20 G/C 1.00 0.67 (0.47–0.97) 1.02 (0.63–1.67) 
  rs3743343 3 UTR 0.24 T/C 1.00 1.15 (0.87–1.52) 1.10 (0.77–1.58) 
  rs3784681 Intronic 0.29 G/C 0.96 0.91 (0.71–1.17) 0.79 (0.56–1.11) 
  rs3825977 Intronic 0.19 C/T 1.00 1.01 (0.72–1.42) 0.76 (0.47–1.24) 
  rs4147358 Intronic 0.22 C/A 0.96 0.99 (0.73–1.33) 0.89 (0.60–1.33) 
  rs4601989 Intronic 0.24 C/T 0.68 0.81 (0.60–1.08) 0.66 (0.44–1.00) 
  rs4776881 Intronic 0.44 T/C 1.00 1.07 (0.89–1.30) 1.21 (0.92–1.60) 
  rs4776890 Intronic 0.40 T/G 0.96 0.97 (0.80–1.19) 0.96 (0.72–1.28) 
  rs4776892 Intronic 0.18 A/T 0.45 1.00 (0.67–1.48) 1.14 (0.69–1.88) 
  rs7163381 Intronic 0.26 G/A 1.00 0.79 (0.61–1.03) 1.06 (0.74–1.51) 
  rs7173811 Intronic 0.47 C/T 0.96 1.06 (0.88–1.28) 0.96 (0.73–1.26) 
  rs7176870 Intronic 0.43 A/G 1.00 1.06 (0.88–1.29) 1.19 (0.90–1.58) 
  rs7181556 Intronic 0.24 C/T 0.99 0.91 (0.69–1.22) 0.75 (0.50–1.12) 
  rs7183244 Intronic 0.39 C/T 0.84 1.00 (0.81–1.23) 1.04 (0.77–1.41) 
  rs9972423 Intronic 0.37 T/A 1.00 0.97 (0.79–1.20) 1.27 (0.94–1.73) 
  rs11071933 Intronic 0.33 C/G 1.00 0.94 (0.75–1.16) 0.92 (0.68–1.25) 
  rs11637581 Intronic 0.28 C/T 0.95 0.98 (0.76–1.28) 1.11 (0.77–1.59) 
  rs11639295 Intronic 0.31 C/T 1.00 0.88 (0.70–1.12) 0.83 (0.59–1.16) 
  rs12102171 Intronic 0.17 C/T 0.86 0.90 (0.62–1.30) 0.84 (0.49–1.43) 
  rs12708492 Intronic 0.48 C/T 1.00 1.02 (0.85–1.24) 1.10 (0.84–1.44) 
  rs12901071 Intronic 0.34 A/G 0.68 0.67 (0.53–0.84) 0.85 (0.60–1.20) 
  rs12904944 Intronic 0.34 G/A 1.00 0.81 (0.64–1.01) 1.07 (0.79–1.47) 
  rs12907997 Intronic 0.50 C/T 1.00 0.95 (0.78–1.14) 0.91 (0.70–1.20) 
  rs12915039 Intronic 0.24 A/C 1.00 1.01 (0.76–1.35) 1.08 (0.72–1.61) 
  rs16950687 Intronic 0.28 A/G 1.00 0.93 (0.72–1.21) 1.22 (0.85–1.76) 
  rs17293443 Intronic 0.22 T/C 0.92 0.85 (0.61–1.19) 1.74 (1.08–2.82) 
Smad4 18q21.1 rs8096092 Intronic 0.38 C/A 0.68 1.00 (0.81–1.23) 1.17 (0.87–1.59) 
  rs10502913 Intronic 0.24 G/A 0.74 0.81 (0.61–1.08) 1.09 (0.72–1.67) 
Smad7 18q21.1 rs1316447 Intronic 0.19 C/T 1.00 0.86 (0.60–1.23) 0.88 (0.53–1.45) 
  rs2337106 Intronic 0.47 C/G 1.00 0.88 (0.72–1.06) 1.11 (0.85–1.45) 
  rs2337107 Intronic 0.41 G/A 0.99 1.12 (0.92–1.36) 0.97 (0.74–1.28) 
  rs3736242 Intronic 0.22 G/A 1.00 0.94 (0.69–1.28) 1.33 (0.84–2.09) 
  rs3764482 Intronic 0.19 C/T 1.00 1.25 (0.86–1.81) 0.60 (0.34–1.07) 
  rs4464148 Intronic 0.31 T/C 1.00 1.06 (0.83–1.35) 0.76 (0.54–1.09) 
  rs4939827 Intronic 0.49 T/C 1.00 0.79 (0.66–0.95) 0.95 (0.73–1.23) 
  rs4939832 Intronic 0.24 A/G 1.00 1.00 (0.76–1.32) 1.29 (0.87–1.92) 
  rs7238442 Intronic 0.46 T/C 0.82 1.12 (0.93–1.35) 0.92 (0.71–1.20) 
  rs12456328 Intronic 0.13 C/T 1.00 0.81 (0.49–1.33) 1.16 (0.51–2.66) 
  rs12953717 Intronic 0.42 C/T 1.00 1.36 (1.12–1.65) 0.90 (0.68–1.19) 
TGFβ1 19q13.1 rs1800469 5 upstream 0.31 G/A 1.00 0.65 (0.51–0.84) 0.98 (0.71–1.38) 
  rs4803455 Intronic 0.48 C/A 0.92 1.43 (1.18–1.73) 1.04 (0.79–1.35) 
TGFβR1 9q22 rs1571590 Intronic 0.20 A/G 0.67 1.39 (0.98–1.96) 1.42 (0.85–2.39) 
  rs6478974 Intronic 0.49 T/A 1.00 0.86 (0.71–1.04) 0.84 (0.63–1.10) 
  rs10733710 Intronic 0.20 G/A 0.96 1.06 (0.77–1.46) 1.22 (0.78–1.91) 

NOTE: MAF and FDR-adjusted Hardy-Weinberg Equilibrium (FDR HWE) based on White control population. ORs are adjusted for age, center, race, and sex.

The contents of this manuscript are solely the responsibility of the authors and do not necessarily represent the official view of the National Cancer Institute.

We thank Drs. Bette J. Caan, Kristin Anderson, and John D. Potter, Sandra Edwards, Roger Edwards, Leslie Palmer, Donna Schaffer, and Judy Morse for data management and collection.

This study was funded by NCI grants CA48998 and CA61757. This research also was supported by the Utah Cancer Registry, which is funded by contract #N01-PC-67000 from the NCI, with additional support from the State of Utah Department of Health, the Northern California Cancer Registry, and the Sacramento Tumor Registry.

The costs of publication of this article were defrayed in part by the payment of page charges. This article must therefore be hereby marked advertisement in accordance with 18 U.S.C. Section 1734 solely to indicate this fact.

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